Table2_Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.docx

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.全膝关节置换术 (total knee arthroplasty)中，胫股假体 (tibiofemoral components)的精准对线是提升膝关节使用寿命与功能的关键因素。然而，利用肌肉骨骼多体动力学模型 (musculoskeletal multibody dynamics model)快速预测全膝关节置换术后胫股假体旋转不良的生物力学响应，仍是一项颇具挑战性的任务。本研究的核心目标为，对比深度学习方法与四种传统机器学习方法，以预测全膝关节置换术后行走步态 (walking gait)中，不同胫骨假体旋转不良 (tibial component malrotation)情况下的膝关节生物力学特性。首先，基于构建完成的肌肉骨骼多体动力学模型，计算全膝关节置换术后行走步态中，胫骨假体在前/后倾斜 (anterior/posterior slope)、内/外旋转 (internal/external rotation)、内/外翻 (varus/valgus rotation)三个方向上±5°范围内不同旋转不良状态下的膝关节接触力 (knee contact forces)与运动学 (kinematics)数据。随后，以上述343组生物力学数据集 (dataset)为基础，分别构建深度学习方法与四种传统机器学习方法。最后，将深度学习方法的预测结果与四种传统机器学习方法的预测结果进行对比分析。研究结果表明，相较于四种传统机器学习方法，深度学习方法在预测全膝关节置换术后行走步态中不同胫骨假体旋转不良的膝关节接触力与运动学时，具备更高的预测精度。本研究开发的深度学习方法可快速预测全膝关节置换术后行走步态中，不同胫骨假体旋转不良对应的生物力学响应。该方法可为外科医师与手术机器人提供建立校准安全区的能力，这对于术前手术规划以及术中机器人辅助手术导航中实现精准对线均至关重要。